# Computer_Vision

**Repository Path**: windclub/Computer_Vision

## Basic Information

- **Project Name**: Computer_Vision
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-24
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Computer Vision Course 

## In this course we will cover the following topics:

### 1 Classical Methods in Computer Vision

    1.1 Linear and Non-Linear Filters: Convolution, Bluring, Gradient, Erosion and Dilation.

    1.2 Interpolation, Affine Transformations. Cumulative Sum and Guided Filtering, Guided Upsampling.

    1.3 Local Features: Edge Detectors, Neighborhood Description.

    1.4 Segmentation by Clustering and Graphs: Watershed Algorithm, Graph-Based Aglomerative Clustering, Graph Cuts, Spectral Methods.

    1.5 Textures: Texture synthesis, hole filling.

### 2 Deep Learning for Computer Vision

    2.1 Introduction: Problems. Image Classification and Semantic Segmentation.

    2.2 Texture Synthesis, Style Transfer, Image Analogies.

    2.3 Object Localization, Detection.

    2.4 Segmentation Revisited: Instance Segmentation.

    2.5 Generative Models.


## Bibliography

https://drive.google.com/open?id=1QchjDgcB4FNF8FQp8DLJeZnHWUZWLGYL